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AdaMD: Adaptive mapping and DVFS for energy-efficient heterogeneous multi-cores

AdaMD: Adaptive mapping and DVFS for energy-efficient heterogeneous multi-cores
AdaMD: Adaptive mapping and DVFS for energy-efficient heterogeneous multi-cores
Modern heterogeneous multi-core systems, containing various types of cores, are increasingly dealing with con current execution of dynamic application workloads. Moreover, the performance constraints of each application vary, and applications enter/exit the system at any time. Existing approaches are not efficient in such dynamic scenarios, especially if applications are unknown, as they require extensive offline application analysis and do not consider the runtime execution scenarios (application arrival/completion, and workload and performance variations) for runtime management. To address this, we present AdaMD, an adaptive mapping and dynamic voltage and frequency scaling (DVFS) approach for improving energy consumption and performance. The key feature of the proposed approach is the elimination of dependency on offline-profiled results while making runtime decisions. This is achieved through a performance prediction model having a maximum error of 7.9% lower than the previously reported model and a mapping approach that allocates processing cores to applications while respecting performance constraints. Furthermore, AdaMD adapts to runtime execution scenarios efficiently by monitoring the application status, and performance/workload variations to adjust the previous DVFS settings and thread to-core mappings. The proposed approach is experimentally validated on the Odroid-XU3, with various combinations of diverse multi-threaded applications from PARSEC and SPLASH benchmarks. Results show energy savings of up to 28% compared to the recently proposed approach while meeting performance constraints.
Run-time management, Energy savings, Heterogeneous multi-cores, Multi-threaded applications
0278-0070
Basireddy, Karunakar R.
5bfb0b2e-8242-499a-a52b-e813d9a90889
Singh, Amit Kumar
bded7886-24ab-4a24-8539-f8fe106426ac
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d
Merrett, Geoff V.
89b3a696-41de-44c3-89aa-b0aa29f54020
Basireddy, Karunakar R.
5bfb0b2e-8242-499a-a52b-e813d9a90889
Singh, Amit Kumar
bded7886-24ab-4a24-8539-f8fe106426ac
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d
Merrett, Geoff V.
89b3a696-41de-44c3-89aa-b0aa29f54020

Basireddy, Karunakar R., Singh, Amit Kumar, Al-Hashimi, Bashir and Merrett, Geoff V. (2019) AdaMD: Adaptive mapping and DVFS for energy-efficient heterogeneous multi-cores. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. (doi:10.1109/TCAD.2019.2935065).

Record type: Article

Abstract

Modern heterogeneous multi-core systems, containing various types of cores, are increasingly dealing with con current execution of dynamic application workloads. Moreover, the performance constraints of each application vary, and applications enter/exit the system at any time. Existing approaches are not efficient in such dynamic scenarios, especially if applications are unknown, as they require extensive offline application analysis and do not consider the runtime execution scenarios (application arrival/completion, and workload and performance variations) for runtime management. To address this, we present AdaMD, an adaptive mapping and dynamic voltage and frequency scaling (DVFS) approach for improving energy consumption and performance. The key feature of the proposed approach is the elimination of dependency on offline-profiled results while making runtime decisions. This is achieved through a performance prediction model having a maximum error of 7.9% lower than the previously reported model and a mapping approach that allocates processing cores to applications while respecting performance constraints. Furthermore, AdaMD adapts to runtime execution scenarios efficiently by monitoring the application status, and performance/workload variations to adjust the previous DVFS settings and thread to-core mappings. The proposed approach is experimentally validated on the Odroid-XU3, with various combinations of diverse multi-threaded applications from PARSEC and SPLASH benchmarks. Results show energy savings of up to 28% compared to the recently proposed approach while meeting performance constraints.

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More information

Accepted/In Press date: 27 July 2019
e-pub ahead of print date: 13 August 2019
Keywords: Run-time management, Energy savings, Heterogeneous multi-cores, Multi-threaded applications

Identifiers

Local EPrints ID: 433327
URI: https://eprints.soton.ac.uk/id/eprint/433327
ISSN: 0278-0070
PURE UUID: 3f0f2a06-38d6-44d4-9f03-8a7ddfbbbe46
ORCID for Karunakar R. Basireddy: ORCID iD orcid.org/0000-0001-9755-1041
ORCID for Geoff V. Merrett: ORCID iD orcid.org/0000-0003-4980-3894

Catalogue record

Date deposited: 14 Aug 2019 16:30
Last modified: 26 Nov 2019 01:45

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Contributors

Author: Karunakar R. Basireddy ORCID iD
Author: Amit Kumar Singh
Author: Bashir Al-Hashimi
Author: Geoff V. Merrett ORCID iD

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